Limit of a nonpreferential attachment multitype network model

Here, we deal with a model of multitype network with nonpreferential attachment growth. The connection between two nodes depends asymmetrically on their types, reflecting the implication of time order in temporal networks. Based upon graph limit theory, we analytically determined the limit of the network model characterized by a kernel, in the sense that the number of copies of any fixed subgraph converges when network size tends to infinity. The results are confirmed by extensive simulations. Our work thus provides a theoretical framework for quantitatively understanding grown temporal complex networks as a whole.

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Complex networks for rainfall modeling: Spatial connections, temporal scale, and network size

Journal of Hydrology ◽

10.1016/j.jhydrol.2017.09.030 ◽

2017 ◽

Vol 554 ◽

pp. 482-489 ◽

Cited By ~ 11

Author(s):

Sanjeev Kumar Jha ◽

Bellie Sivakumar

Keyword(s):

Complex Networks ◽

Network Size ◽

Temporal Scale

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Synchronization in a Novel Local-World Dynamical Network Model

Mathematical Problems in Engineering ◽

10.1155/2014/851403 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Jianeng Tang ◽

Peizhong Liu

Keyword(s):

Complex Networks ◽

Complex Network ◽

Network Model ◽

Optimal Growth ◽

Dynamical Networks ◽

Dynamical Network ◽

Network Research ◽

Topology Model ◽

The Relationship ◽

Random Failures

Advances in complex network research have recently stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. In the paper, we study the synchronizability of a class of local-world dynamical networks. Then, we have proposed a local-world synchronization-optimal growth topology model. Compared with the local-world evolving network model, it exhibits a stronger synchronizability. We also investigate the robustness of the synchronizability with respect to random failures and the fragility of the synchronizability with specific removal of nodes.

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AN EVOLVING NETWORK MODEL OF CREDIT RISK CONTAGION IN THE FINANCIAL MARKET

Technological and Economic Development of Economy ◽

10.3846/20294913.2015.1095808 ◽

2016 ◽

Vol 23 (1) ◽

pp. 22-37 ◽

Cited By ~ 13

Author(s):

Tingqiang CHEN ◽

Jianmin HE ◽

Xindan LI

Keyword(s):

Theoretical Analysis ◽

Risk Aversion ◽

Credit Risk ◽

Network Model ◽

Financial Market ◽

Network Size ◽

Clustering Coefficient ◽

The Other ◽

Credit Risk Contagion ◽

Average Fitness

This paper introduces an evolving network model of credit risk contagion containing the average fitness of credit risk contagion, the risk aversion sentiments, and the ability of resist risk of credit risk holders. We discuss the effects of the aforementioned factors on credit risk contagion in the financial market through a series of theoretical analysis and numerical simulations. We find that, on one hand, the infected path distribution of the network gradually increases with the increase in the average fitness of credit risk contagion and the risk aversion sentiments of nodes, but gradually decreases with the increase in the ability to resist risk of nodes. On the other hand, the average fitness of credit risk contagion and the risk aversion sentiments of nodes increase the average clustering coefficient of nodes, whereas the ability to resist risk of nodes decreases this coefficient. Moreover, network size also decreases the average clustering coefficient.

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Network model of bilateral power markets based on complex networks

International Journal of Modern Physics B ◽

10.1142/s0217979214501446 ◽

2014 ◽

Vol 28 (22) ◽

pp. 1450144 ◽

Cited By ~ 1

Author(s):

Yang Wu ◽

Junyong Liu ◽

Furong Li ◽

Zhanxin Yan ◽

Li Zhang

Keyword(s):

Complex Networks ◽

Network Model ◽

Simulation Analysis ◽

Electric Power Industry ◽

Clustering Coefficient ◽

Power Markets ◽

Cascading Failure ◽

Topological Stability ◽

Market Organization ◽

Topological Robustness

The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.

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Thermodynamic Analysis of Time Evolving Networks

Entropy ◽

10.3390/e20100759 ◽

2018 ◽

Vol 20 (10) ◽

pp. 759 ◽

Cited By ~ 4

Author(s):

Cheng Ye ◽

Richard Wilson ◽

Luca Rossi ◽

Andrea Torsello ◽

Edwin Hancock

Keyword(s):

Complex Networks ◽

Network Size ◽

Von Neumann Entropy ◽

Von Neumann ◽

Evolving Networks ◽

Thermodynamic Framework ◽

Network Time ◽

Abrupt Changes ◽

Consecutive Time ◽

Thermodynamic Variables

The problem of how to represent networks, and from this representation, derive succinct characterizations of network structure and in particular how this structure evolves with time, is of central importance in complex network analysis. This paper tackles the problem by proposing a thermodynamic framework to represent the structure of time-varying complex networks. More importantly, such a framework provides a powerful tool for better understanding the network time evolution. Specifically, the method uses a recently-developed approximation of the network von Neumann entropy and interprets it as the thermodynamic entropy for networks. With an appropriately-defined internal energy in hand, the temperature between networks at consecutive time points can be readily derived, which is computed as the ratio of change of entropy and change in energy. It is critical to emphasize that one of the main advantages of the proposed method is that all these thermodynamic variables can be computed in terms of simple network statistics, such as network size and degree statistics. To demonstrate the usefulness of the thermodynamic framework, the paper uses real-world network data, which are extracted from time-evolving complex systems in the financial and biological domains. The experimental results successfully illustrate that critical events, including abrupt changes and distinct periods in the evolution of complex networks, can be effectively characterized.

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Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality

Entropy ◽

10.3390/e21010086 ◽

2019 ◽

Vol 21 (1) ◽

pp. 86 ◽

Cited By ~ 2

Author(s):

C. Martínez-Martínez ◽

J. Méndez-Bermúdez

Keyword(s):

Numerical Simulations ◽

Network Model ◽

Information Entropy ◽

Spectral Properties ◽

Random Network ◽

Tight Binding ◽

Network Connectivity ◽

Network Size ◽

Random Networks ◽

Scaling And Universality

We study the localization properties of the eigenvectors, characterized by their information entropy, of tight-binding random networks with balanced losses and gain. The random network model, which is based on Erdős–Rényi (ER) graphs, is defined by three parameters: the network size N, the network connectivity α , and the losses-and-gain strength γ . Here, N and α are the standard parameters of ER graphs, while we introduce losses and gain by including complex self-loops on all vertices with the imaginary amplitude i γ with random balanced signs, thus breaking the Hermiticity of the corresponding adjacency matrices and inducing complex spectra. By the use of extensive numerical simulations, we define a scaling parameter ξ ≡ ξ ( N , α , γ ) that fixes the localization properties of the eigenvectors of our random network model; such that, when ξ < 0.1 ( 10 < ξ ), the eigenvectors are localized (extended), while the localization-to-delocalization transition occurs for 0.1 < ξ < 10 . Moreover, to extend the applicability of our findings, we demonstrate that for fixed ξ , the spectral properties (characterized by the position of the eigenvalues on the complex plane) of our network model are also universal; i.e., they do not depend on the specific values of the network parameters.

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Building Complex Network Similar to Facebook

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.909 ◽

2014 ◽

Vol 513-517 ◽

pp. 909-913

Author(s):

Dong Wei Guo ◽

Xiang Yan Meng ◽

Cai Fang Hou

Keyword(s):

Social Networks ◽

Social Network ◽

Complex Networks ◽

Complex Network ◽

Network Model ◽

Clustering Coefficient ◽

Network Characteristics ◽

Modeling Methods ◽

Community Strength ◽

Strength Based

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

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An Empirical Study on the Network Model and the Online Knowledge Production Structure

10.4018/978-1-6684-3702-5.ch030 ◽

2022 ◽

pp. 599-611

Author(s):

Quan Chen ◽

Jiangtao Wang ◽

Ruiqiu Ou ◽

Sang-Bing Tsai

Keyword(s):

Complex Network ◽

Network Model ◽

Knowledge Production ◽

Mass Production ◽

Network Size ◽

Software System ◽

Production Structure ◽

R Software ◽

Knowledge Propagation ◽

New Perspective

Mass production has attracted much attention as a new approach to knowledge production. The R software system is a typical product of mass production. For its unique architecture, the R software system accurately recorded the natural process of knowledge propagation and inheritance. Thus, this article established a dynamic complex network model based on the derivative relationship between R software packages, which reflects the evolution process of online knowledge production structure in R software system, and studied the process of knowledge propagation and inheritance via the dynamic complex network analysis method. These results show that the network size increases with time, reflecting the tendency of R software to accelerate the accumulation of knowledge. The network density and network cohesion decrease with the increase of scale, indicating that the knowledge structure of R software presents a trend of expansion. The unique extension structure of R software provides a rich research foundation for the propagation of knowledge; thus, the results can provide us a new perspective for knowledge discovery and technological innovation.

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